Live Streamer Portrait Segmentation Dataset

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Project Overview:

Objective

We embarked on a journey to create a dataset tailored for the segmentation of live streamers’ portraits. This endeavor is instrumental in enhancing real-time background removal, augmented reality applications, and overall streaming quality. Moreover, our dataset stands as a testament to our expertise in collecting and annotating high-value data for machine-learning models. Additionally, it serves as a cornerstone for future advancements in the field, providing a solid foundation for further research and innovation.

Scope

Our team focused on capturing an array of live-streaming scenarios. Consequently, we delved into diverse backgrounds, lighting conditions, and streamer appearances, ensuring our dataset comprehensively addresses various portrait segmentation challenges. Additionally, utilizing recorded sessions from popular platforms like Twitch, YouTube Live, and Facebook Live, we gathered content across multiple genres including gaming, chatting, music performances, and tutorials, in a plethora of streaming environments.

Sources

Recorded live streaming sessions from popular platforms such as Twitch, YouTube Live, and Facebook Live (with permissions) can offer valuable insights into audience engagement, content performance, and trending topics. Moreover, they provide an opportunity to analyze viewer interactions, gauge audience sentiment, and identify areas for improvement.
Different genres of streams: Gaming, chatting, music performances, tutorials, and more. Furthermore, you can engage in live gaming sessions, lively chatting, captivating music performances, insightful tutorials, and much more. Additionally, explore a variety of content including gaming, chatting, music performances, tutorials, and more. Moreover, delve into a world of entertainment with gaming, chatting, music performances, tutorials, and more.
Streaming setups come in a variety of styles. Some creators opt for green screens, allowing them to superimpose themselves onto any background they choose. Others prefer cluttered backgrounds, which can add a sense of authenticity or personality to their streams.
Collaborations with streamers for dedicated capture sessions involve a myriad of benefits. Firstly, they offer a unique opportunity to engage with a broader audience. Secondly, they facilitate the creation of dynamic content that can be shared across various platforms. Additionally, they foster a sense of community by bringing together different gaming communities.

Data Collection Metrics

Gaming Streams: 250,000 frames
Chatting & Music Streams: 200,000 frames
Tutorial Streams: 150,000 frames
Diverse Lighting Setups: 100,000 frames

Annotation Process

Stages

Raw Data Refinement: Frames not meeting our quality standards were excluded.
Streamer Portrait Segmentation: Additionally, Streamer Portrait Segmentation involves the precise delineation of streamers from their backgrounds.
Accessory Annotations: Furthermore, Accessory Annotations play a crucial role in identifying and segmenting streamer accessories like headphones and instruments.
Quality Check: Lastly, Quality Check procedures are implemented to ensure annotation accuracy and completeness.

Annotation Metrics

Streamer Portrait Segmentations: 700,000
Accessory Annotations: 350,000
Annotations Reviewed for Quality Assurance: 140,000

Quality Assurance

Stages

Expert Reviews by video production specialists and streamers.
Automated Consistency Checks for potential errors.
Inter-annotator Reviews to maintain uniformity.

QA Metrics

Annotations Evaluated by Experts: 140,000
Discrepancies Detected and Rectified: 14,000

Conclusion

The Live Streamer Portrait Segmentation Dataset is a groundbreaking contribution from our team. It is poised to redefine the visual experience in live streaming, enhancing real-time video manipulations, stream quality, and immersive AR implementations. This project is a shining example of our capability to handle complex data collection and annotation tasks, propelling forward the fields of machine learning and artificial intelligence.